As we’ve recently started feeling that response times of one of our webapps got worse, we decided to spend some time tweaking the apps’ performance. As a first step, we wanted to get a thorough understanding of current response times. For performance evaluations, using minimum, maximum or average response times is a bad idea: “The ‘average’ is the evil of performance optimization and often as helpful as ‘average patient temperature in the hospital'” (MySQL Performance Blog). Instead, performance tuners should be looking at the percentile: “A percentile is the value of a variable below which a certain percent of observations fall” (Wikipedia). In other words: the 95th percentile is the time in which 95% of requests finished. Therefore, a performance goals related to the percentile could be similar to “The 95th percentile should be lower than 800 ms”. Setting such performance goals is one thing, but efficiently tracking them for a live system is another one.

I’ve spent quite some time looking for existing implementations of percentile calculations (e.g. 1, 2). All of them required storing response times for each and every request and calculate the percentile on demand or adding new response times in order. This was not what I wanted. I was hoping for a solution that would allow memory and CPU efficient live statistics for hundreds of thousands of requests. Storing response times for hundreds of thousands of requests and calculating the percentile on demand does neither sound CPU nor memory efficient.

Such a solution as I was hoping for simply seems not to exist. On second thought, I came up with another idea: For the type of performance evaluation I was looking for, it’s not necessary to get the exact percentile. An approximate answer like “the 95th percentile is between 850ms and 900ms” would totally suffice. Lowering the requirements this way makes an implementation extremely easy, especially if upper and lower borders for the possible results are known. For example, I’m not interested in response times higher than several seconds – they are extremely bad anyway, regardless of being 10 seconds or 15 seconds.

This approach only requires two int values (= 8 byte) per bucket, allowing to track 128 buckets with 1K of memory. More than sufficient for analysing response times of a web application using a granularity of 50ms). Additionally, for the sake of performance, I’ve intentionally implemented this without any synchronization(e.g. using AtomicIntegers), knowing that some increments might get lost.

By the way, using Google Charts and 60 percentile counters, I was able to create a nice graph out of one hour of collected response times:

@Dag Thanks, fixed that. I’veuploaded two additional files: (IntervalPercentileChartImage.java and GoogleChartModel.java) While these files require some effort to make them compile, they should give a very good start to generate Google Chart URLs from PecentileCounters. That’s the extra effort you could have avoided with a small donation – you’d be surprised what people do for beer 😀 (After doing this yourself, I’d be very happy to upload the changed/working version though)

Initially performance stats for fetching data for a web service from a SQLite DB. Used to profile & compare in memory vs. on disk. The percentiles and the historic view gave a huge amount of insight over what I was originally getting from averages

Going forward I’ll be using it to track the performance of many different things, web services, db calls, proprietary endpoints, message queues etc